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–Improve vectorization in the 'norm' functions

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This commit is contained in:
ChipKerchner 2019-08-27 12:15:19 -05:00
parent d513fb4c8e
commit 288e6f9c07
2 changed files with 77 additions and 13 deletions
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89
modules/core/src/norm.cpp
View File

@ -63,7 +63,31 @@ int normHamming(const uchar* a, int n, int cellSize)
return -1;
int i = 0;
int result = 0;
#if CV_ENABLE_UNROLLED
#if CV_SIMD
v_uint64 t = vx_setzero_u64();
if ( cellSize == 2)
{
v_uint16 mask = v_reinterpret_as_u16(vx_setall_u8(0x55));
for(; i <= n - v_uint8::nlanes; i += v_uint8::nlanes)
{
v_uint16 a0 = v_reinterpret_as_u16(vx_load(a + i));
t += v_popcount(v_reinterpret_as_u64((a0 | (a0 >> 1)) & mask));
}
}
else // cellSize == 4
{
v_uint16 mask = v_reinterpret_as_u16(vx_setall_u8(0x11));
for(; i <= n - v_uint8::nlanes; i += v_uint8::nlanes)
{
v_uint16 a0 = v_reinterpret_as_u16(vx_load(a + i));
v_uint16 a1 = a0 | (a0 >> 2);
t += v_popcount(v_reinterpret_as_u64((a1 | (a1 >> 1)) & mask));
}
}
result += (int)v_reduce_sum(t);
vx_cleanup();
#elif CV_ENABLE_UNROLLED
for( ; i <= n - 4; i += 4 )
result += tab[a[i]] + tab[a[i+1]] + tab[a[i+2]] + tab[a[i+3]];
#endif
@ -85,7 +109,30 @@ int normHamming(const uchar* a, const uchar* b, int n, int cellSize)
return -1;
int i = 0;
int result = 0;
#if CV_ENABLE_UNROLLED
#if CV_SIMD
v_uint64 t = vx_setzero_u64();
if ( cellSize == 2)
{
v_uint16 mask = v_reinterpret_as_u16(vx_setall_u8(0x55));
for(; i <= n - v_uint8::nlanes; i += v_uint8::nlanes)
{
v_uint16 ab0 = v_reinterpret_as_u16(vx_load(a + i) ^ vx_load(b + i));
t += v_popcount(v_reinterpret_as_u64((ab0 | (ab0 >> 1)) & mask));
}
}
else // cellSize == 4
{
v_uint16 mask = v_reinterpret_as_u16(vx_setall_u8(0x11));
for(; i <= n - v_uint8::nlanes; i += v_uint8::nlanes)
{
v_uint16 ab0 = v_reinterpret_as_u16(vx_load(a + i) ^ vx_load(b + i));
v_uint16 ab1 = ab0 | (ab0 >> 2);
t += v_popcount(v_reinterpret_as_u64((ab1 | (ab1 >> 1)) & mask));
}
}
result += (int)v_reduce_sum(t);
vx_cleanup();
#elif CV_ENABLE_UNROLLED
for( ; i <= n - 4; i += 4 )
result += tab[a[i] ^ b[i]] + tab[a[i+1] ^ b[i+1]] +
tab[a[i+2] ^ b[i+2]] + tab[a[i+3] ^ b[i+3]];
@ -99,13 +146,20 @@ float normL2Sqr_(const float* a, const float* b, int n)
{
int j = 0; float d = 0.f;
#if CV_SIMD
v_float32 v_d = vx_setzero_f32();
for (; j <= n - v_float32::nlanes; j += v_float32::nlanes)
v_float32 v_d0 = vx_setzero_f32(), v_d1 = vx_setzero_f32();
v_float32 v_d2 = vx_setzero_f32(), v_d3 = vx_setzero_f32();
for (; j <= n - 4 * v_float32::nlanes; j += 4 * v_float32::nlanes)
{
v_float32 t = vx_load(a + j) - vx_load(b + j);
v_d = v_muladd(t, t, v_d);
v_float32 t0 = vx_load(a + j) - vx_load(b + j);
v_float32 t1 = vx_load(a + j + v_float32::nlanes) - vx_load(b + j + v_float32::nlanes);
v_float32 t2 = vx_load(a + j + 2 * v_float32::nlanes) - vx_load(b + j + 2 * v_float32::nlanes);
v_float32 t3 = vx_load(a + j + 3 * v_float32::nlanes) - vx_load(b + j + 3 * v_float32::nlanes);
v_d0 = v_muladd(t0, t0, v_d0);
v_d1 = v_muladd(t1, t1, v_d1);
v_d2 = v_muladd(t2, t2, v_d2);
v_d3 = v_muladd(t3, t3, v_d3);
}
d = v_reduce_sum(v_d);
d = v_reduce_sum(v_d0 + v_d1 + v_d2 + v_d3);
#endif
for( ; j < n; j++ )
{
@ -120,10 +174,16 @@ float normL1_(const float* a, const float* b, int n)
{
int j = 0; float d = 0.f;
#if CV_SIMD
v_float32 v_d = vx_setzero_f32();
for (; j <= n - v_float32::nlanes; j += v_float32::nlanes)
v_d += v_absdiff(vx_load(a + j), vx_load(b + j));
d = v_reduce_sum(v_d);
v_float32 v_d0 = vx_setzero_f32(), v_d1 = vx_setzero_f32();
v_float32 v_d2 = vx_setzero_f32(), v_d3 = vx_setzero_f32();
for (; j <= n - 4 * v_float32::nlanes; j += 4 * v_float32::nlanes)
{
v_d0 += v_absdiff(vx_load(a + j), vx_load(b + j));
v_d1 += v_absdiff(vx_load(a + j + v_float32::nlanes), vx_load(b + j + v_float32::nlanes));
v_d2 += v_absdiff(vx_load(a + j + 2 * v_float32::nlanes), vx_load(b + j + 2 * v_float32::nlanes));
v_d3 += v_absdiff(vx_load(a + j + 3 * v_float32::nlanes), vx_load(b + j + 3 * v_float32::nlanes));
}
d = v_reduce_sum(v_d0 + v_d1 + v_d2 + v_d3);
#endif
for( ; j < n; j++ )
d += std::abs(a[j] - b[j]);
@ -134,8 +194,11 @@ int normL1_(const uchar* a, const uchar* b, int n)
{
int j = 0, d = 0;
#if CV_SIMD
for (; j <= n - v_uint8::nlanes; j += v_uint8::nlanes)
d += v_reduce_sad(vx_load(a + j), vx_load(b + j));
for (; j <= n - 4 * v_uint8::nlanes; j += 4 * v_uint8::nlanes)
d += v_reduce_sad(vx_load(a + j), vx_load(b + j)) +
v_reduce_sad(vx_load(a + j + v_uint8::nlanes), vx_load(b + j + v_uint8::nlanes)) +
v_reduce_sad(vx_load(a + j + 2 * v_uint8::nlanes), vx_load(b + j + 2 * v_uint8::nlanes)) +
v_reduce_sad(vx_load(a + j + 3 * v_uint8::nlanes), vx_load(b + j + 3 * v_uint8::nlanes));
#endif
for( ; j < n; j++ )
d += std::abs(a[j] - b[j]);

1
modules/core/src/stat.simd.hpp
View File

@ -39,6 +39,7 @@ int normHamming(const uchar* a, int n)
for (; i <= n - v_uint8::nlanes; i += v_uint8::nlanes)
t += v_popcount(v_reinterpret_as_u64(vx_load(a + i)));
result = (int)v_reduce_sum(t);
vx_cleanup();
}
#endif